Modeling difficulty
Modeling objective difficulty for motor task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 5201.1 5226.3 -2596.6 5193.1 4048
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3526 -0.8300 -0.4242 0.8932 2.8790
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.6054 0.7781
## Number of obs: 4052, groups: IDjoueur, 137
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0751 0.1152 -9.332 < 2e-16 ***
## difficulty 3.0681 0.1759 17.439 < 2e-16 ***
## timeNorm -0.4561 0.1217 -3.748 0.000178 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.529
## timeNorm -0.443 -0.179
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
##
## Logique2 Motrice Sensoriel
## 0 4052 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.9902403
## 1st Qu.:-0.4701721
## Median : 0.0033326
## Mean : 0.0007788
## 3rd Qu.: 0.3936276
## Max. : 1.8862696
## [1] "Intercept: -1.08 1e-20 ***"
## [1] "Difficulty: 3.07 4.1e-68 ***"
## [1] "Time: -0.456 0.00018 ***"
## [1] "R2 fixed: 0.15"
## [1] "R2 mixed: 0.28"
## [1] "Cross Val: 0.64"
## [1] "AIC: 5200"
## 0% 25% 50% 75% 100%
## -1.886269575 -0.393627612 -0.003332599 0.470172110 1.990240334

## 0% 25% 50% 75% 100%
## -1.886269575 -0.393627612 -0.003332599 0.470172110 1.990240334



## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'gam'

Modeling objective difficulty for sensory task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 4130.5 4155.6 -2061.2 4122.5 3988
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.3516 -0.6336 0.0687 0.6637 6.7192
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 1.306 1.143
## Number of obs: 3992, groups: IDjoueur, 135
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.5991 0.1595 -16.298 <2e-16 ***
## difficulty 8.5059 0.3294 25.820 <2e-16 ***
## timeNorm -0.2737 0.1429 -1.915 0.0555 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.577
## timeNorm -0.360 -0.175
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
##
## Logique2 Motrice Sensoriel
## 0 0 3992
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-2.657865
## 1st Qu.:-0.735389
## Median : 0.028691
## Mean : 0.001613
## 3rd Qu.: 0.626099
## Max. : 3.372937
## [1] "Intercept: -2.6 1e-59 ***"
## [1] "Difficulty: 8.51 5.2e-147 ***"
## [1] "Time: -0.274 0.056 ."
## [1] "R2 fixed: 0.51"
## [1] "R2 mixed: 0.65"
## [1] "Cross Val: 0.75"
## [1] "AIC: 4100"
## 0% 25% 50% 75% 100%
## -3.37293734 -0.62609925 -0.02869066 0.73538942 2.65786548

## 0% 25% 50% 75% 100%
## -3.37293734 -0.62609925 -0.02869066 0.73538942 2.65786548



## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'gam'

Modeling objective difficulty for logical task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 4534.1 4559.3 -2263.0 4526.1 3988
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7316 -0.6985 -0.2545 0.7441 6.5713
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 1.716 1.31
## Number of obs: 3992, groups: IDjoueur, 135
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7838 0.1550 -11.51 <2e-16 ***
## difficulty 5.5087 0.2316 23.79 <2e-16 ***
## timeNorm -1.5119 0.1494 -10.12 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.439
## timeNorm -0.234 -0.428
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
##
## Logique2 Motrice Sensoriel
## 3992 0 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-2.533846
## 1st Qu.:-0.912974
## Median :-0.198655
## Mean : 0.004174
## 3rd Qu.: 1.039877
## Max. : 2.880385
## [1] "Intercept: -1.78 1.2e-30 ***"
## [1] "Difficulty: 5.51 4.5e-125 ***"
## [1] "Time: -1.51 4.6e-24 ***"
## [1] "R2 fixed: 0.33"
## [1] "R2 mixed: 0.56"
## [1] "Cross Val: 0.71"
## [1] "AIC: 4500"
## 0% 25% 50% 75% 100%
## -2.8803846 -1.0398766 0.1986546 0.9129742 2.5338456

## 0% 25% 50% 75% 100%
## -2.8803846 -1.0398766 0.1986546 0.9129742 2.5338456



## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'gam'

Influence of Objective difficulty on Subjective Difficulty
All tasks
## obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
## 1: 0.03125 4.342716e-05 316 87 0.057 .
## 2: 0.09375 3.384905e-05 312 201 0.17 :(
## 3: 0.15625 7.829454e-05 303 311 0.69 :(
## 4: 0.21875 2.149488e-02 319 444 0.036 *
## 5: 0.28125 -2.904618e-05 286 625 0.92 :(
## 6: 0.34375 9.885961e-03 260 840 0.5 :(
## 7: 0.40625 3.925970e-02 274 899 0.0097 **
## 8: 0.46875 8.233885e-02 267 941 7.6e-07 ***
## 9: 0.53125 1.038859e-01 234 847 7e-11 ***
## 10: 0.59375 1.098386e-01 284 635 1.5e-11 ***
## 11: 0.65625 1.214842e-01 258 511 2.7e-12 ***
## 12: 0.71875 1.142805e-01 305 313 2.7e-10 ***
## 13: 0.78125 1.099682e-01 332 166 1e-06 ***
## 14: 0.84375 9.531926e-02 305 108 0.002 **
## 15: 0.90625 1.211802e-05 339 48 0.52 :(
## 16: 0.96875 3.477634e-05 652 14 0.88 :(

## obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
## 1: 0.03125 2.419693e-05 291 70 0.8 :(
## 2: 0.09375 -2.141665e-05 226 88 0.22 :(
## 3: 0.15625 -1.612657e-05 196 91 0.15 :(
## 4: 0.21875 -2.043126e-02 203 131 0.32 :(
## 5: 0.28125 -7.135355e-02 177 166 0.0024 **
## 6: 0.34375 1.423581e-02 123 195 0.37 :(
## 7: 0.40625 7.931007e-02 138 191 0.0023 **
## 8: 0.46875 1.094841e-01 129 174 0.00035 ***
## 9: 0.53125 1.357438e-01 123 197 4.4e-07 ***
## 10: 0.59375 7.619211e-02 149 145 0.003 **
## 11: 0.65625 1.020189e-01 116 106 0.0045 **
## 12: 0.71875 1.429148e-01 126 64 0.00013 ***
## 13: 0.78125 2.377444e-02 127 32 0.57 :(
## 14: 0.84375 1.428174e-01 91 18 0.017 *
## 15: 0.90625 -7.142305e-02 107 11 0.38 :(
## 16: 0.96875 0.000000e+00 230 1 <NA>
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties

## obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
## 1: 0.03125 1.427794e-01 25 17 0.00057 ***
## 2: 0.09375 3.545860e-05 84 111 0.00025 ***
## 3: 0.15625 5.239006e-02 106 149 0.0095 **
## 4: 0.21875 4.382500e-05 105 220 0.44 :(
## 5: 0.28125 -1.187233e-02 99 281 0.39 :(
## 6: 0.34375 -7.620086e-02 118 421 0.0054 **
## 7: 0.40625 -2.699148e-05 122 488 0.9 :(
## 8: 0.46875 1.111250e-01 111 510 0.00016 ***
## 9: 0.53125 1.189819e-01 86 394 7.9e-05 ***
## 10: 0.59375 1.809004e-01 99 285 2.2e-10 ***
## 11: 0.65625 1.904793e-01 101 206 1.6e-08 ***
## 12: 0.71875 1.071804e-01 142 139 0.0017 **
## 13: 0.78125 9.517804e-02 153 86 0.0047 **
## 14: 0.84375 1.061886e-05 145 55 0.37 :(
## 15: 0.90625 -2.038385e-02 130 23 0.29 :(
## 16: 0.96875 0.000000e+00 259 5 <NA>
## Warning: Removed 1 rows containing missing values (geom_point).

## obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
## 1: 0.03125 0.000000e+00 0 0 <NA>
## 2: 0.09375 0.000000e+00 2 2 <NA>
## 3: 0.15625 0.000000e+00 1 71 <NA>
## 4: 0.21875 7.138258e-02 11 93 0.3 :(
## 5: 0.28125 0.000000e+00 10 178 <NA>
## 6: 0.34375 2.999858e-01 19 224 0.00065 ***
## 7: 0.40625 1.904980e-01 14 220 0.099 .
## 8: 0.46875 -2.377865e-02 27 257 0.76 :(
## 9: 0.53125 -4.762544e-05 25 256 0.97 :(
## 10: 0.59375 -8.576225e-02 36 205 0.084 .
## 11: 0.65625 7.138877e-02 41 199 0.14 :(
## 12: 0.71875 1.530535e-01 37 110 0.00077 ***
## 13: 0.78125 1.428764e-01 52 48 0.0033 **
## 14: 0.84375 7.147380e-02 69 35 0.11 :(
## 15: 0.90625 4.286188e-01 102 14 0.00057 ***
## 16: 0.96875 0.000000e+00 163 8 <NA>
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).

Motor task
## obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
## 1: 0.03125 0.000000e+00 0 0 <NA>
## 2: 0.09375 0.000000e+00 18 3 <NA>
## 3: 0.15625 2.618201e-05 92 22 0.44 :(
## 4: 0.21875 -4.082045e-05 145 75 0.25 :(
## 5: 0.28125 -7.117675e-03 135 175 0.19 :(
## 6: 0.34375 2.375475e-02 127 329 0.15 :(
## 7: 0.40625 3.568020e-02 138 444 0.1 :(
## 8: 0.46875 9.520208e-02 126 441 0.0016 **
## 9: 0.53125 1.548070e-01 126 374 8.9e-10 ***
## 10: 0.59375 1.784991e-01 136 263 7.9e-09 ***
## 11: 0.65625 2.499345e-01 133 159 2.9e-10 ***
## 12: 0.71875 2.856551e-01 162 63 9.2e-07 ***
## 13: 0.78125 3.928219e-01 152 18 1.1e-06 ***
## 14: 0.84375 0.000000e+00 92 4 <NA>
## 15: 0.90625 0.000000e+00 70 0 <NA>
## 16: 0.96875 0.000000e+00 30 0 <NA>
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_point).

## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
## 1: 0.03125 0.000000e+00 0 0 <NA>
## 2: 0.09375 0.000000e+00 18 3 <NA>
## 3: 0.15625 3.396854e-05 86 21 0.47 :(
## 4: 0.21875 -6.670841e-06 100 50 0.35 :(
## 5: 0.28125 -1.190892e-01 82 80 0.00072 ***
## 6: 0.34375 6.118190e-02 49 99 0.36 :(
## 7: 0.40625 1.076873e-01 65 103 0.014 *
## 8: 0.46875 1.428563e-01 58 81 0.0072 **
## 9: 0.53125 1.786171e-01 63 106 1.1e-06 ***
## 10: 0.59375 1.428118e-01 65 60 0.015 *
## 11: 0.65625 4.000764e-01 49 44 9.5e-05 ***
## 12: 0.71875 6.932262e-06 58 12 0.62 :(
## 13: 0.78125 0.000000e+00 32 1 <NA>
## 14: 0.84375 0.000000e+00 0 0 <NA>
## 15: 0.90625 0.000000e+00 0 0 <NA>
## 16: 0.96875 0.000000e+00 0 0 <NA>
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 6 rows containing missing values (geom_point).
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties

## obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
## 1: 0.03125 0.000000e+00 0 0 <NA>
## 2: 0.09375 0.000000e+00 0 0 <NA>
## 3: 0.15625 0.000000e+00 6 1 <NA>
## 4: 0.21875 -5.098858e-06 45 25 0.36 :(
## 5: 0.28125 4.541290e-06 53 95 0.31 :(
## 6: 0.34375 2.143476e-05 76 210 0.53 :(
## 7: 0.40625 -2.131138e-05 70 299 0.64 :(
## 8: 0.46875 2.861704e-02 61 318 0.25 :(
## 9: 0.53125 1.428899e-01 54 223 6e-05 ***
## 10: 0.59375 2.143013e-01 53 125 2.4e-07 ***
## 11: 0.65625 3.332932e-01 64 71 3.3e-07 ***
## 12: 0.71875 3.333021e-01 84 32 1.9e-06 ***
## 13: 0.78125 0.000000e+00 88 9 <NA>
## 14: 0.84375 0.000000e+00 60 2 <NA>
## 15: 0.90625 0.000000e+00 11 0 <NA>
## 16: 0.96875 0.000000e+00 0 0 <NA>
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties

## obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
## 1: 0.03125 0.0000000 0 0 <NA>
## 2: 0.09375 0.0000000 0 0 <NA>
## 3: 0.15625 0.0000000 0 0 <NA>
## 4: 0.21875 0.0000000 0 0 <NA>
## 5: 0.28125 0.0000000 0 0 <NA>
## 6: 0.34375 0.0000000 2 20 <NA>
## 7: 0.40625 0.0000000 3 42 <NA>
## 8: 0.46875 0.0000000 7 42 <NA>
## 9: 0.53125 0.0000000 9 45 <NA>
## 10: 0.59375 -0.0857674 18 78 0.12 :(
## 11: 0.65625 -0.0238951 20 44 0.53 :(
## 12: 0.71875 0.1053921 20 19 0.35 :(
## 13: 0.78125 0.0000000 32 8 <NA>
## 14: 0.84375 0.0000000 32 2 <NA>
## 15: 0.90625 0.0000000 59 0 <NA>
## 16: 0.96875 0.0000000 30 0 <NA>
## Warning: Removed 9 rows containing missing values (geom_point).
## Warning: Removed 9 rows containing missing values (geom_point).

Sensory task
## obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
## 1: 0.03125 0.000000e+00 160 32 0.00073 ***
## 2: 0.09375 5.473331e-05 139 95 3.5e-05 ***
## 3: 0.15625 4.350596e-05 97 153 0.94 :(
## 4: 0.21875 6.609701e-05 67 175 0.3 :(
## 5: 0.28125 -3.565305e-02 70 225 0.065 .
## 6: 0.34375 -3.964448e-02 56 252 0.13 :(
## 7: 0.40625 -1.026174e-02 54 187 0.52 :(
## 8: 0.46875 2.379528e-02 59 235 0.6 :(
## 9: 0.53125 -7.382765e-05 43 227 0.76 :(
## 10: 0.59375 -7.137519e-02 53 166 0.097 .
## 11: 0.65625 -4.404848e-05 54 195 0.72 :(
## 12: 0.71875 -1.905050e-02 59 137 0.43 :(
## 13: 0.78125 -3.568004e-02 79 93 0.4 :(
## 14: 0.84375 -1.287757e-05 87 81 0.94 :(
## 15: 0.90625 4.676427e-05 142 43 0.69 :(
## 16: 0.96875 1.784999e-02 463 14 0.31 :(

## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
## 1: 0.03125 0.000000e+00 146 19 0.013 *
## 2: 0.09375 1.910045e-05 91 20 0.2 :(
## 3: 0.15625 4.514092e-05 51 16 0.91 :(
## 4: 0.21875 -9.520158e-02 37 21 0.15 :(
## 5: 0.28125 -4.985487e-05 49 19 0.56 :(
## 6: 0.34375 9.384204e-05 32 23 0.53 :(
## 7: 0.40625 -1.714910e-01 31 21 0.034 *
## 8: 0.46875 2.385828e-02 34 18 0.15 :(
## 9: 0.53125 7.756459e-05 25 22 0.83 :(
## 10: 0.59375 -3.265245e-01 35 22 0.00025 ***
## 11: 0.65625 -1.633143e-01 37 17 0.018 *
## 12: 0.71875 1.904531e-01 31 14 0.037 *
## 13: 0.78125 -1.428628e-01 49 14 0.034 *
## 14: 0.84375 4.808356e-05 51 13 0.67 :(
## 15: 0.90625 0.000000e+00 86 10 <NA>
## 16: 0.96875 0.000000e+00 230 1 <NA>
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties

## obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
## 1: 0.03125 1.198414e-01 14 13 0.0044 **
## 2: 0.09375 4.279747e-07 48 75 0.00014 ***
## 3: 0.15625 6.254372e-05 46 83 0.1 :(
## 4: 0.21875 6.122825e-05 28 105 0.87 :(
## 5: 0.28125 -3.565279e-02 20 105 0.16 :(
## 6: 0.34375 -1.818199e-01 21 128 9e-04 ***
## 7: 0.40625 1.428710e-01 23 110 0.069 .
## 8: 0.46875 2.856472e-01 24 123 0.00061 ***
## 9: 0.53125 6.006544e-05 15 109 0.96 :(
## 10: 0.59375 1.428976e-01 18 100 0.057 .
## 11: 0.65625 7.137593e-02 16 89 0.55 :(
## 12: 0.71875 -3.912901e-05 28 71 0.86 :(
## 13: 0.78125 1.827680e-05 30 54 0.65 :(
## 14: 0.84375 6.070557e-06 35 41 0.89 :(
## 15: 0.90625 -7.362921e-05 56 19 0.17 :(
## 16: 0.96875 0.000000e+00 216 5 <NA>
## Warning: Removed 1 rows containing missing values (geom_point).

## obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
## 1: 0.03125 0 0 0 NA
## 2: 0.09375 0 0 0 NA
## 3: 0.15625 0 0 54 NA
## 4: 0.21875 0 2 49 NA
## 5: 0.28125 0 1 101 NA
## 6: 0.34375 0 3 101 NA
## 7: 0.40625 0 0 56 NA
## 8: 0.46875 0 1 94 NA
## 9: 0.53125 0 3 96 NA
## 10: 0.59375 0 0 44 NA
## 11: 0.65625 0 1 89 NA
## 12: 0.71875 0 0 52 NA
## 13: 0.78125 0 0 25 NA
## 14: 0.84375 0 1 27 NA
## 15: 0.90625 0 0 14 NA
## 16: 0.96875 0 17 8 NA
## Warning: Removed 15 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).

Logical task
## obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
## 1: 0.03125 4.512536e-05 156 55 0.84 :(
## 2: 0.09375 -4.846520e-05 155 103 0.87 :(
## 3: 0.15625 2.385238e-02 114 136 0.081 .
## 4: 0.21875 7.150552e-02 107 194 0.017 *
## 5: 0.28125 4.764744e-02 81 225 0.14 :(
## 6: 0.34375 3.332034e-05 77 259 0.75 :(
## 7: 0.40625 7.138641e-02 82 268 0.044 *
## 8: 0.46875 1.071922e-01 82 265 0.0015 **
## 9: 0.53125 1.190605e-01 65 246 0.0046 **
## 10: 0.59375 1.072026e-01 95 206 0.00072 ***
## 11: 0.65625 1.428217e-01 71 157 0.0037 **
## 12: 0.71875 1.429032e-01 84 113 4.8e-05 ***
## 13: 0.78125 7.149725e-02 101 55 0.055 .
## 14: 0.84375 2.142376e-01 126 23 0.00079 ***
## 15: 0.90625 0.000000e+00 127 5 <NA>
## 16: 0.96875 0.000000e+00 159 0 <NA>
## Warning: Removed 2 rows containing missing values (geom_point).

## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
## 1: 0.03125 -5.584448e-06 145 51 0.73 :(
## 2: 0.09375 -2.027060e-05 117 65 0.6 :(
## 3: 0.15625 -3.045394e-05 59 54 0.87 :(
## 4: 0.21875 4.170671e-05 66 60 0.72 :(
## 5: 0.28125 2.859194e-02 46 67 0.5 :(
## 6: 0.34375 4.286626e-02 42 73 0.51 :(
## 7: 0.40625 2.142231e-01 42 67 0.00036 ***
## 8: 0.46875 1.428347e-01 37 75 0.048 *
## 9: 0.53125 1.427869e-01 35 69 0.011 *
## 10: 0.59375 1.667295e-01 49 63 0.00035 ***
## 11: 0.65625 1.277801e-06 30 45 0.85 :(
## 12: 0.71875 1.071069e-01 37 38 0.053 .
## 13: 0.78125 1.429283e-01 46 17 0.051 .
## 14: 0.84375 0.000000e+00 40 5 <NA>
## 15: 0.90625 0.000000e+00 21 1 <NA>
## 16: 0.96875 0.000000e+00 0 0 <NA>
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties

## obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
## 1: 0.03125 0.000000e+00 11 4 <NA>
## 2: 0.09375 5.723969e-05 36 36 0.17 :(
## 3: 0.15625 1.071864e-01 54 65 0.11 :(
## 4: 0.21875 2.496434e-01 32 90 2e-04 ***
## 5: 0.28125 -7.383892e-06 26 81 0.73 :(
## 6: 0.34375 -1.666531e-01 21 83 0.094 .
## 7: 0.40625 -1.428407e-01 29 79 0.035 *
## 8: 0.46875 1.428168e-01 26 69 0.066 .
## 9: 0.53125 1.456272e-02 17 62 0.47 :(
## 10: 0.59375 7.139054e-02 28 60 0.19 :(
## 11: 0.65625 1.071899e-01 21 46 0.15 :(
## 12: 0.71875 9.481998e-05 30 36 0.4 :(
## 13: 0.78125 -2.380948e-02 35 23 0.39 :(
## 14: 0.84375 1.143389e-01 50 12 0.18 :(
## 15: 0.90625 0.000000e+00 63 4 <NA>
## 16: 0.96875 0.000000e+00 43 0 <NA>
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = subj.diff.rnd, y = subj.diff.dda,
## conf.int = T, : cannot compute exact confidence intervals with ties

## obj.diff.bin subj.diff.delta.rnd.dda n.rnd n.dda pval
## 1: 0.03125 0.00000000 0 0 <NA>
## 2: 0.09375 0.00000000 2 2 <NA>
## 3: 0.15625 0.00000000 1 17 <NA>
## 4: 0.21875 0.00000000 9 44 <NA>
## 5: 0.28125 0.00000000 9 77 <NA>
## 6: 0.34375 0.29767829 14 103 6.7e-05 ***
## 7: 0.40625 0.23805085 11 122 0.018 *
## 8: 0.46875 0.08337811 19 121 0.11 :(
## 9: 0.53125 0.07143520 13 115 0.23 :(
## 10: 0.59375 0.02853096 18 83 0.57 :(
## 11: 0.65625 0.28567004 20 66 0.0019 **
## 12: 0.71875 0.35710077 17 39 3.8e-06 ***
## 13: 0.78125 0.14288629 20 15 0.03 *
## 14: 0.84375 0.00000000 36 6 <NA>
## 15: 0.90625 0.00000000 43 0 <NA>
## 16: 0.96875 0.00000000 116 0 <NA>
## Warning: Removed 5 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_point).

Influence of Playtime on Subjective Difficulty Error
For all groups, motor, sensitive and logical
## Warning: Removed 2370 rows containing non-finite values (stat_bin2d).

## Warning: Removed 2310 rows containing non-finite values (stat_bin2d).

## Warning: Removed 2310 rows containing non-finite values (stat_bin2d).

##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7101 -0.1860 -0.0144 0.1830 0.6941
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.24752 0.01826 13.556 <2e-16 ***
## timeNorm 0.03373 0.02285 1.476 0.14
## obj.diff -0.56227 0.01983 -28.351 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06545186)
##
## Null deviance: 166.13 on 1681 degrees of freedom
## Residual deviance: 109.89 on 1679 degrees of freedom
## (2310 observations deleted due to missingness)
## AIC: 192.43
##
## Number of Fisher Scoring iterations: 2
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.4840110 -0.050845983 353 0.053 .
## 2: 4.5 NA 0.5009994 -0.024626313 411 0.21 :(
## 3: 7.5 NA 0.4940760 -0.019878578 411 0.32 :(
## 4: 10.5 NA 0.4938841 0.011167138 411 0.6 :(
## 5: 13.5 NA 0.4840366 -0.001073084 411 0.95 :(
## 6: 16.5 NA 0.4943476 -0.002958823 411 0.89 :(
## 7: 19.5 NA 0.4964643 -0.033397611 411 0.07 .
## 8: 22.5 NA 0.4846435 -0.010529685 411 0.6 :(
## 9: 25.5 NA 0.4802931 -0.001036981 411 0.96 :(
## 10: 28.5 NA 0.4629047 0.009977788 411 0.63 :(
## time error.diff shapes
## 1: 1.5 -0.050845983 16
## 2: 4.5 -0.024626313 16
## 3: 7.5 -0.019878578 16
## 4: 10.5 0.011167138 16
## 5: 13.5 -0.001073084 16
## 6: 16.5 -0.002958823 16
## 7: 19.5 -0.033397611 16
## 8: 22.5 -0.010529685 16
## 9: 25.5 -0.001036981 16
## 10: 28.5 0.009977788 16

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.2432479 0.12917006 80 0.053 .
## 2: 4.5 NA 0.2562790 0.14093419 76 0.0031 **
## 3: 7.5 NA 0.2598344 0.07993451 80 0.0097 **
## 4: 10.5 NA 0.2634087 0.07199009 80 0.032 *
## 5: 13.5 NA 0.2534150 0.06403160 73 0.027 *
## 6: 16.5 NA 0.2556853 0.06466362 71 0.089 .
## 7: 19.5 NA 0.2547723 0.02113616 96 0.42 :(
## 8: 22.5 NA 0.2431613 0.07633368 83 0.026 *
## 9: 25.5 NA 0.2438608 0.12507700 79 0.00085 ***
## 10: 28.5 NA 0.2354276 0.08287540 83 0.0022 **
## time error.diff shapes
## 1: 1.5 0.12917006 16
## 2: 4.5 0.14093419 24
## 3: 7.5 0.07993451 24
## 4: 10.5 0.07199009 24
## 5: 13.5 0.06403160 24
## 6: 16.5 0.06466362 16
## 7: 19.5 0.02113616 16
## 8: 22.5 0.07633368 24
## 9: 25.5 0.12507700 24
## 10: 28.5 0.08287540 24
## Warning: Removed 3 rows containing missing values (geom_errorbar).

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.4795679 0.03054758 207 0.4 :(
## 2: 4.5 NA 0.4897145 -0.03719132 258 0.15 :(
## 3: 7.5 NA 0.4849785 -0.02554995 254 0.31 :(
## 4: 10.5 NA 0.4913258 0.06037396 269 0.036 *
## 5: 13.5 NA 0.4754936 0.04222385 277 0.18 :(
## 6: 16.5 NA 0.4903172 0.04683914 274 0.12 :(
## 7: 19.5 NA 0.5014546 -0.02187366 228 0.45 :(
## 8: 22.5 NA 0.4839376 0.02305690 256 0.45 :(
## 9: 25.5 NA 0.4896862 -0.01617450 278 0.48 :(
## 10: 28.5 NA 0.4808808 0.04489820 285 0.11 :(
## time error.diff shapes
## 1: 1.5 0.03054758 16
## 2: 4.5 -0.03719132 16
## 3: 7.5 -0.02554995 16
## 4: 10.5 0.06037396 24
## 5: 13.5 0.04222385 16
## 6: 16.5 0.04683914 16
## 7: 19.5 -0.02187366 16
## 8: 22.5 0.02305690 16
## 9: 25.5 -0.01617450 16
## 10: 28.5 0.04489820 16

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.7897802 -0.17785100 66 9.9e-06 ***
## 2: 4.5 NA 0.7803532 -0.09749158 77 0.0044 **
## 3: 7.5 NA 0.7674539 -0.08928525 77 0.0077 **
## 4: 10.5 NA 0.8023715 -0.13009804 62 0.0022 **
## 5: 13.5 NA 0.7988200 -0.12530366 61 0.00085 ***
## 6: 16.5 NA 0.7678228 -0.11058229 66 0.0013 **
## 7: 19.5 NA 0.7500808 -0.07733665 87 0.0013 **
## 8: 22.5 NA 0.7655285 -0.12989773 72 0.00029 ***
## 9: 25.5 NA 0.7778278 -0.11969210 54 0.002 **
## 10: 28.5 NA 0.7828447 -0.16338955 43 6e-06 ***
## time error.diff shapes
## 1: 1.5 -0.17785100 24
## 2: 4.5 -0.09749158 24
## 3: 7.5 -0.08928525 24
## 4: 10.5 -0.13009804 24
## 5: 13.5 -0.12530366 24
## 6: 16.5 -0.11058229 24
## 7: 19.5 -0.07733665 24
## 8: 22.5 -0.12989773 24
## 9: 25.5 -0.11969210 24
## 10: 28.5 -0.16338955 24

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.3556457 -0.14643400 347 5.7e-06 ***
## 2: 4.5 NA 0.5101236 -0.10385762 405 1.4e-07 ***
## 3: 7.5 NA 0.5056170 -0.07527783 405 0.00057 ***
## 4: 10.5 NA 0.5365403 -0.08887349 405 3e-05 ***
## 5: 13.5 NA 0.5187050 -0.10083041 405 4e-07 ***
## 6: 16.5 NA 0.5109441 -0.12902174 405 1.6e-07 ***
## 7: 19.5 NA 0.5236235 -0.08423175 405 1.5e-05 ***
## 8: 22.5 NA 0.5423005 -0.07593718 405 0.00012 ***
## 9: 25.5 NA 0.5324158 -0.04607646 405 0.014 *
## 10: 28.5 NA 0.5318888 -0.08687999 405 1e-04 ***
## time error.diff shapes
## 1: 1.5 -0.14643400 24
## 2: 4.5 -0.10385762 24
## 3: 7.5 -0.07527783 24
## 4: 10.5 -0.08887349 24
## 5: 13.5 -0.10083041 24
## 6: 16.5 -0.12902174 24
## 7: 19.5 -0.08423175 24
## 8: 22.5 -0.07593718 24
## 9: 25.5 -0.04607646 24
## 10: 28.5 -0.08687999 24

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.1532464 0.08749165 210 0.11 :(
## 2: 4.5 NA 0.1777012 0.08989797 132 0.098 .
## 3: 7.5 NA 0.1874267 0.09152168 129 0.041 *
## 4: 10.5 NA 0.1880539 0.07469314 108 0.18 :(
## 5: 13.5 NA 0.1923434 0.04290497 115 0.51 :(
## 6: 16.5 NA 0.1903028 -0.01587250 127 0.8 :(
## 7: 19.5 NA 0.1665092 0.02699334 154 0.49 :(
## 8: 22.5 NA 0.1861060 0.11964834 105 0.013 *
## 9: 25.5 NA 0.1962599 0.13955600 119 0.0024 **
## 10: 28.5 NA 0.1847045 0.12594412 118 0.007 **
## time error.diff shapes
## 1: 1.5 0.08749165 16
## 2: 4.5 0.08989797 16
## 3: 7.5 0.09152168 24
## 4: 10.5 0.07469314 16
## 5: 13.5 0.04290497 16
## 6: 16.5 -0.01587250 16
## 7: 19.5 0.02699334 16
## 8: 22.5 0.11964834 24
## 9: 25.5 0.13955600 24
## 10: 28.5 0.12594412 24

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.4419674 -0.17368146 67 0.048 *
## 2: 4.5 NA 0.4890926 -0.03303027 146 0.52 :(
## 3: 7.5 NA 0.4978024 -0.08199182 151 0.13 :(
## 4: 10.5 NA 0.5046928 -0.05553930 168 0.42 :(
## 5: 13.5 NA 0.4780767 -0.13584537 159 0.041 *
## 6: 16.5 NA 0.4984364 -0.15521906 151 0.052 .
## 7: 19.5 NA 0.5045227 -0.06069415 89 0.29 :(
## 8: 22.5 NA 0.5028234 -0.10506038 162 0.048 *
## 9: 25.5 NA 0.4959545 -0.09734223 150 0.17 :(
## 10: 28.5 NA 0.5050058 -0.17334577 153 0.014 *
## time error.diff shapes
## 1: 1.5 -0.17368146 24
## 2: 4.5 -0.03303027 16
## 3: 7.5 -0.08199182 16
## 4: 10.5 -0.05553930 16
## 5: 13.5 -0.13584537 24
## 6: 16.5 -0.15521906 16
## 7: 19.5 -0.06069415 16
## 8: 22.5 -0.10506038 24
## 9: 25.5 -0.09734223 16
## 10: 28.5 -0.17334577 24

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.8802217 -0.2459247 70 9e-09 ***
## 2: 4.5 NA 0.8798108 -0.2387529 127 1.3e-12 ***
## 3: 7.5 NA 0.8434293 -0.2125622 125 1.7e-07 ***
## 4: 10.5 NA 0.8697723 -0.1983531 129 9.8e-09 ***
## 5: 13.5 NA 0.8545178 -0.2015393 131 3.7e-08 ***
## 6: 16.5 NA 0.8464567 -0.2285818 127 6e-09 ***
## 7: 19.5 NA 0.8735961 -0.2107135 162 4e-08 ***
## 8: 22.5 NA 0.8596608 -0.1991268 138 1.5e-07 ***
## 9: 25.5 NA 0.8667667 -0.1664666 136 8.8e-06 ***
## 10: 28.5 NA 0.8683130 -0.2015478 134 5.7e-09 ***
## time error.diff shapes
## 1: 1.5 -0.2459247 24
## 2: 4.5 -0.2387529 24
## 3: 7.5 -0.2125622 24
## 4: 10.5 -0.1983531 24
## 5: 13.5 -0.2015393 24
## 6: 16.5 -0.2285818 24
## 7: 19.5 -0.2107135 24
## 8: 22.5 -0.1991268 24
## 9: 25.5 -0.1664666 24
## 10: 28.5 -0.2015478 24

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.3735435 -0.1321485500 347 7.8e-05 ***
## 2: 4.5 NA 0.4622814 -0.1222423528 405 7.1e-06 ***
## 3: 7.5 NA 0.4719065 -0.0401721647 405 0.11 :(
## 4: 10.5 NA 0.4758802 -0.0005060253 405 0.98 :(
## 5: 13.5 NA 0.4802303 -0.0075009274 405 0.76 :(
## 6: 16.5 NA 0.4878267 -0.0048228149 405 0.84 :(
## 7: 19.5 NA 0.4701706 -0.0063255874 405 0.77 :(
## 8: 22.5 NA 0.4277367 -0.0139269309 405 0.57 :(
## 9: 25.5 NA 0.4256454 0.0502729816 405 0.055 .
## 10: 28.5 NA 0.4219079 0.0452511671 405 0.065 .
## time error.diff shapes
## 1: 1.5 -0.1321485500 24
## 2: 4.5 -0.1222423528 24
## 3: 7.5 -0.0401721647 16
## 4: 10.5 -0.0005060253 16
## 5: 13.5 -0.0075009274 16
## 6: 16.5 -0.0048228149 16
## 7: 19.5 -0.0063255874 16
## 8: 22.5 -0.0139269309 16
## 9: 25.5 0.0502729816 16
## 10: 28.5 0.0452511671 16

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.1444702 0.1292499 186 0.0067 **
## 2: 4.5 NA 0.1797062 0.1736101 156 0.0036 **
## 3: 7.5 NA 0.1891977 0.1661685 135 1.9e-05 ***
## 4: 10.5 NA 0.2000008 0.1817844 126 1.6e-06 ***
## 5: 13.5 NA 0.2005678 0.2039673 109 7.4e-07 ***
## 6: 16.5 NA 0.1940743 0.1654151 102 6.2e-06 ***
## 7: 19.5 NA 0.1751540 0.1389413 163 0.0014 **
## 8: 22.5 NA 0.1856054 0.1361224 153 0.00077 ***
## 9: 25.5 NA 0.1833445 0.1854309 153 3.5e-07 ***
## 10: 28.5 NA 0.1860644 0.1850076 161 1.9e-06 ***
## time error.diff shapes
## 1: 1.5 0.1292499 24
## 2: 4.5 0.1736101 24
## 3: 7.5 0.1661685 24
## 4: 10.5 0.1817844 24
## 5: 13.5 0.2039673 24
## 6: 16.5 0.1654151 24
## 7: 19.5 0.1389413 24
## 8: 22.5 0.1361224 24
## 9: 25.5 0.1854309 24
## 10: 28.5 0.1850076 24
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_errorbar).

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.4986645 -0.088095070 94 0.16 :(
## 2: 4.5 NA 0.4929868 0.036113675 152 0.31 :(
## 3: 7.5 NA 0.4861101 0.058520341 166 0.15 :(
## 4: 10.5 NA 0.4918719 0.050765987 188 0.21 :(
## 5: 13.5 NA 0.4893625 0.024825306 212 0.59 :(
## 6: 16.5 NA 0.4907633 0.039949668 218 0.26 :(
## 7: 19.5 NA 0.5014750 0.052290286 113 0.16 :(
## 8: 22.5 NA 0.4888822 0.011822929 186 0.79 :(
## 9: 25.5 NA 0.4868837 0.067077231 182 0.054 .
## 10: 28.5 NA 0.4888791 -0.007535674 172 0.8 :(
## time error.diff shapes
## 1: 1.5 -0.088095070 16
## 2: 4.5 0.036113675 16
## 3: 7.5 0.058520341 16
## 4: 10.5 0.050765987 16
## 5: 13.5 0.024825306 16
## 6: 16.5 0.039949668 16
## 7: 19.5 0.052290286 16
## 8: 22.5 0.011822929 16
## 9: 25.5 0.067077231 16
## 10: 28.5 -0.007535674 16

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.8339353 -0.3233520 67 5.6e-09 ***
## 2: 4.5 NA 0.8686165 -0.3387834 97 4.9e-14 ***
## 3: 7.5 NA 0.8162133 -0.2506208 104 8.9e-11 ***
## 4: 10.5 NA 0.8248292 -0.2086811 91 1.5e-07 ***
## 5: 13.5 NA 0.8200777 -0.2230776 84 4.2e-08 ***
## 6: 16.5 NA 0.8327977 -0.2036156 85 1.1e-07 ***
## 7: 19.5 NA 0.8155218 -0.2197435 129 2.3e-07 ***
## 8: 22.5 NA 0.8167217 -0.2483378 66 1.4e-06 ***
## 9: 25.5 NA 0.7960266 -0.2401919 70 8.9e-07 ***
## 10: 28.5 NA 0.7892935 -0.1649722 72 4.3e-05 ***
## time error.diff shapes
## 1: 1.5 -0.3233520 24
## 2: 4.5 -0.3387834 24
## 3: 7.5 -0.2506208 24
## 4: 10.5 -0.2086811 24
## 5: 13.5 -0.2230776 24
## 6: 16.5 -0.2036156 24
## 7: 19.5 -0.2197435 24
## 8: 22.5 -0.2483378 24
## 9: 25.5 -0.2401919 24
## 10: 28.5 -0.1649722 24

For all taks, per group
## Warning: Removed 1920 rows containing non-finite values (stat_bin2d).

## Warning: Removed 3390 rows containing non-finite values (stat_bin2d).

## Warning: Removed 1680 rows containing non-finite values (stat_bin2d).

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.5370427 -0.27738939 234 6.7e-09 ***
## 2: 4.5 NA 0.5934534 -0.23994050 255 2.4e-07 ***
## 3: 7.5 NA 0.5884498 -0.17968026 255 0.00012 ***
## 4: 10.5 NA 0.5552892 -0.10234098 255 0.013 *
## 5: 13.5 NA 0.5545696 -0.20286547 255 0.00011 ***
## 6: 16.5 NA 0.5552245 -0.14224554 255 0.0029 **
## 7: 19.5 NA 0.5512077 -0.09181317 255 0.0049 **
## 8: 22.5 NA 0.5340527 -0.15808362 255 0.00026 ***
## 9: 25.5 NA 0.5067738 -0.09377907 255 0.035 *
## 10: 28.5 NA 0.5215155 -0.07756929 255 0.045 *
## time error.diff shapes
## 1: 1.5 -0.27738939 24
## 2: 4.5 -0.23994050 24
## 3: 7.5 -0.17968026 24
## 4: 10.5 -0.10234098 24
## 5: 13.5 -0.20286547 24
## 6: 16.5 -0.14224554 24
## 7: 19.5 -0.09181317 24
## 8: 22.5 -0.15808362 24
## 9: 25.5 -0.09377907 24
## 10: 28.5 -0.07756929 24

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.4609367 -0.1068665217 198 0.029 *
## 2: 4.5 NA 0.4887974 -0.0105738942 302 0.68 :(
## 3: 7.5 NA 0.4757123 -0.0075705560 277 0.83 :(
## 4: 10.5 NA 0.4915204 0.0335529297 286 0.31 :(
## 5: 13.5 NA 0.4713214 -0.0006543756 306 0.96 :(
## 6: 16.5 NA 0.4868129 -0.0105873764 317 0.77 :(
## 7: 19.5 NA 0.4910932 -0.0383665766 174 0.28 :(
## 8: 22.5 NA 0.4812844 0.0030439336 271 0.9 :(
## 9: 25.5 NA 0.4878047 -0.0113157054 274 0.75 :(
## 10: 28.5 NA 0.4781364 -0.0031963553 283 0.93 :(
## time error.diff shapes
## 1: 1.5 -0.1068665217 24
## 2: 4.5 -0.0105738942 16
## 3: 7.5 -0.0075705560 16
## 4: 10.5 0.0335529297 16
## 5: 13.5 -0.0006543756 16
## 6: 16.5 -0.0105873764 16
## 7: 19.5 -0.0383665766 16
## 8: 22.5 0.0030439336 16
## 9: 25.5 -0.0113157054 16
## 10: 28.5 -0.0031963553 16

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.3378392 -0.0711598943 344 0.0025 **
## 2: 4.5 NA 0.3919380 -0.0276459632 432 0.098 .
## 3: 7.5 NA 0.4163719 -0.0228318805 432 0.19 :(
## 4: 10.5 NA 0.4580837 -0.0103905222 432 0.52 :(
## 5: 13.5 NA 0.4517138 -0.0075860836 432 0.69 :(
## 6: 16.5 NA 0.4502447 -0.0114558951 432 0.51 :(
## 7: 19.5 NA 0.4442288 -0.0001713636 432 0.99 :(
## 8: 22.5 NA 0.4363635 -0.0151055495 432 0.3 :(
## 9: 25.5 NA 0.4515881 0.0176739953 432 0.22 :(
## 10: 28.5 NA 0.4315254 0.0095706668 432 0.58 :(
## time error.diff shapes
## 1: 1.5 -0.0711598943 24
## 2: 4.5 -0.0276459632 16
## 3: 7.5 -0.0228318805 16
## 4: 10.5 -0.0103905222 16
## 5: 13.5 -0.0075860836 16
## 6: 16.5 -0.0114558951 16
## 7: 19.5 -0.0001713636 16
## 8: 22.5 -0.0151055495 16
## 9: 25.5 0.0176739953 16
## 10: 28.5 0.0095706668 16

Per group, motor task
## Warning: Removed 300 rows containing non-finite values (stat_bin2d).

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.7401079 -0.23309532 46 0.001 **
## 2: 4.5 NA 0.6826685 -0.17221114 54 0.0053 **
## 3: 7.5 NA 0.6595850 -0.13510640 54 0.014 *
## 4: 10.5 NA 0.6528757 -0.15958834 54 0.0072 **
## 5: 13.5 NA 0.6614892 -0.21081771 54 3e-05 ***
## 6: 16.5 NA 0.6339280 -0.15806752 54 0.019 *
## 7: 19.5 NA 0.6203989 -0.07992182 54 0.084 .
## 8: 22.5 NA 0.6266104 -0.13166951 54 0.029 *
## 9: 25.5 NA 0.6285569 -0.14267184 54 0.0048 **
## 10: 28.5 NA 0.6137866 -0.12264488 54 0.027 *
## time error.diff shapes
## 1: 1.5 -0.23309532 24
## 2: 4.5 -0.17221114 24
## 3: 7.5 -0.13510640 24
## 4: 10.5 -0.15958834 24
## 5: 13.5 -0.21081771 24
## 6: 16.5 -0.15806752 24
## 7: 19.5 -0.07992182 16
## 8: 22.5 -0.13166951 24
## 9: 25.5 -0.14267184 24
## 10: 28.5 -0.12264488 24

## Warning: Removed 1410 rows containing non-finite values (stat_bin2d).

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.4688771 -0.068257715 156 0.35 :(
## 2: 4.5 NA 0.4911750 -0.041551932 170 0.34 :(
## 3: 7.5 NA 0.4766640 -0.046651368 156 0.25 :(
## 4: 10.5 NA 0.4818171 0.044933433 150 0.26 :(
## 5: 13.5 NA 0.4653591 0.028769131 164 0.48 :(
## 6: 16.5 NA 0.4849413 0.001858205 163 0.99 :(
## 7: 19.5 NA 0.4839334 -0.106874013 111 0.046 *
## 8: 22.5 NA 0.4613620 -0.037131958 137 0.47 :(
## 9: 25.5 NA 0.4744244 -0.041490985 145 0.35 :(
## 10: 28.5 NA 0.4676360 0.009744705 154 0.8 :(
## time error.diff shapes
## 1: 1.5 -0.068257715 16
## 2: 4.5 -0.041551932 16
## 3: 7.5 -0.046651368 16
## 4: 10.5 0.044933433 16
## 5: 13.5 0.028769131 16
## 6: 16.5 0.001858205 16
## 7: 19.5 -0.106874013 24
## 8: 22.5 -0.037131958 16
## 9: 25.5 -0.041490985 16
## 10: 28.5 0.009744705 16

## Warning: Removed 660 rows containing non-finite values (stat_bin2d).

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.3625068 0.022757467 116 0.54 :(
## 2: 4.5 NA 0.3890090 0.044145162 141 0.12 :(
## 3: 7.5 NA 0.4235370 0.003441036 141 0.92 :(
## 4: 10.5 NA 0.4219971 0.086443724 141 0.0022 **
## 5: 13.5 NA 0.4090663 0.053875029 141 0.058 .
## 6: 16.5 NA 0.4242053 0.058616310 141 0.03 *
## 7: 19.5 NA 0.4661068 0.035242400 141 0.19 :(
## 8: 22.5 NA 0.4567958 0.055267337 141 0.047 *
## 9: 25.5 NA 0.4380129 0.047888295 141 0.056 .
## 10: 28.5 NA 0.4030005 0.077680680 141 0.0029 **
## time error.diff shapes
## 1: 1.5 0.022757467 16
## 2: 4.5 0.044145162 16
## 3: 7.5 0.003441036 16
## 4: 10.5 0.086443724 24
## 5: 13.5 0.053875029 16
## 6: 16.5 0.058616310 24
## 7: 19.5 0.035242400 16
## 8: 22.5 0.055267337 24
## 9: 25.5 0.047888295 16
## 10: 28.5 0.077680680 24

Per group, sensory task
## Warning: Removed 810 rows containing non-finite values (stat_bin2d).
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.3571483 -0.06453945 83 1 :(
## 2: 4.5 NA 0.4905779 -0.47939642 84 0.25 :(
## 3: 7.5 NA 0.5073238 -0.36747499 84 0.25 :(
## 4: 10.5 NA 0.4815897 -0.02603997 84 1 :(
## 5: 13.5 NA 0.4820886 -0.31835154 84 0.5 :(
## 6: 16.5 NA 0.5032217 -0.33836142 84 0.5 :(
## 7: 19.5 NA 0.5002064 -0.29686222 84 0.5 :(
## 8: 22.5 NA 0.5266886 -0.32525332 84 0.5 :(
## 9: 25.5 NA 0.4735542 -0.13970225 84 0.75 :(
## 10: 28.5 NA 0.5185295 -0.35656381 84 0.25 :(
## time error.diff shapes
## 1: 1.5 -0.06453945 16
## 2: 4.5 -0.47939642 16
## 3: 7.5 -0.36747499 16
## 4: 10.5 -0.02603997 16
## 5: 13.5 -0.31835154 16
## 6: 16.5 -0.33836142 16
## 7: 19.5 -0.29686222 16
## 8: 22.5 -0.32525332 16
## 9: 25.5 -0.13970225 16
## 10: 28.5 -0.35656381 16
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 10 rows containing missing values (geom_errorbar).

## Warning: Removed 1230 rows containing non-finite values (stat_bin2d).

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.3985712 -0.14747456 22 0.44 :(
## 2: 4.5 NA 0.5101423 -0.06860877 81 0.24 :(
## 3: 7.5 NA 0.4807701 0.05523892 71 0.38 :(
## 4: 10.5 NA 0.5086218 -0.13870469 88 0.27 :(
## 5: 13.5 NA 0.4643639 -0.07882054 82 0.49 :(
## 6: 16.5 NA 0.4894083 -0.11720531 81 0.32 :(
## 7: 19.5 NA 0.4967375 0.03943897 39 0.62 :(
## 8: 22.5 NA 0.4963117 0.05916008 81 0.38 :(
## 9: 25.5 NA 0.5030194 0.06158308 74 0.65 :(
## 10: 28.5 NA 0.4879182 -0.01426288 75 1 :(
## time error.diff shapes
## 1: 1.5 -0.14747456 16
## 2: 4.5 -0.06860877 16
## 3: 7.5 0.05523892 16
## 4: 10.5 -0.13870469 16
## 5: 13.5 -0.07882054 16
## 6: 16.5 -0.11720531 16
## 7: 19.5 0.03943897 16
## 8: 22.5 0.05916008 16
## 9: 25.5 0.06158308 16
## 10: 28.5 -0.01426288 16
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## Warning: Removed 270 rows containing non-finite values (stat_bin2d).

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.4464811 -0.15244831 97 0.00029 ***
## 2: 4.5 NA 0.4938329 -0.08901972 132 0.00014 ***
## 3: 7.5 NA 0.4780872 -0.07169067 132 0.0088 **
## 4: 10.5 NA 0.5638033 -0.09219219 132 0.00084 ***
## 5: 13.5 NA 0.5307551 -0.08087389 132 0.00015 ***
## 6: 16.5 NA 0.5140122 -0.11152315 132 2.2e-05 ***
## 7: 19.5 NA 0.4956298 -0.05253809 132 0.0054 **
## 8: 22.5 NA 0.5211902 -0.08677411 132 0.00026 ***
## 9: 25.5 NA 0.5705292 -0.02530146 132 0.15 :(
## 10: 28.5 NA 0.5353820 -0.08634180 132 0.0016 **
## time error.diff shapes
## 1: 1.5 -0.15244831 24
## 2: 4.5 -0.08901972 24
## 3: 7.5 -0.07169067 24
## 4: 10.5 -0.09219219 24
## 5: 13.5 -0.08087389 24
## 6: 16.5 -0.11152315 24
## 7: 19.5 -0.05253809 24
## 8: 22.5 -0.08677411 24
## 9: 25.5 -0.02530146 16
## 10: 28.5 -0.08634180 24

Per group, logical task
## Warning: Removed 810 rows containing non-finite values (stat_bin2d).

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.5902832 -0.32468140 105 1.7e-06 ***
## 2: 4.5 NA 0.6261366 -0.29040113 117 1.8e-05 ***
## 3: 7.5 NA 0.6138626 -0.18468766 117 0.01 *
## 4: 10.5 NA 0.5631616 -0.07414257 117 0.23 :(
## 5: 13.5 NA 0.5572596 -0.16823972 117 0.041 *
## 6: 16.5 NA 0.5562352 -0.11099977 117 0.095 .
## 7: 19.5 NA 0.5558895 -0.09079836 117 0.036 *
## 8: 22.5 NA 0.4966207 -0.17819780 117 0.0054 **
## 9: 25.5 NA 0.4744163 -0.03838009 117 0.43 :(
## 10: 28.5 NA 0.4810727 -0.01969924 117 0.69 :(
## time error.diff shapes
## 1: 1.5 -0.32468140 24
## 2: 4.5 -0.29040113 24
## 3: 7.5 -0.18468766 24
## 4: 10.5 -0.07414257 16
## 5: 13.5 -0.16823972 24
## 6: 16.5 -0.11099977 16
## 7: 19.5 -0.09079836 24
## 8: 22.5 -0.17819780 24
## 9: 25.5 -0.03838009 16
## 10: 28.5 -0.01969924 16
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## Warning: Removed 750 rows containing non-finite values (stat_bin2d).

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.4676032 -0.141829825 20 0.067 .
## 2: 4.5 NA 0.4469716 0.077115519 51 0.2 :(
## 3: 7.5 NA 0.4655608 0.045104317 50 0.52 :(
## 4: 10.5 NA 0.4904902 0.096365996 48 0.0098 **
## 5: 13.5 NA 0.4971273 -0.017073289 60 0.5 :(
## 6: 16.5 NA 0.4881123 0.002154773 73 1 :(
## 7: 19.5 NA 0.5150354 0.039099590 24 0.54 :(
## 8: 22.5 NA 0.5098155 0.030573864 53 0.7 :(
## 9: 25.5 NA 0.5026097 0.032340956 55 0.28 :(
## 10: 28.5 NA 0.4944961 -0.024980434 54 0.54 :(
## time error.diff shapes
## 1: 1.5 -0.141829825 16
## 2: 4.5 0.077115519 16
## 3: 7.5 0.045104317 16
## 4: 10.5 0.096365996 24
## 5: 13.5 -0.017073289 16
## 6: 16.5 0.002154773 16
## 7: 19.5 0.039099590 16
## 8: 22.5 0.030573864 16
## 9: 25.5 0.032340956 16
## 10: 28.5 -0.024980434 16
## Warning: Removed 3 rows containing missing values (geom_errorbar).

## Warning: Removed 750 rows containing non-finite values (stat_bin2d).

## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 NA 0.2355514 -0.074437040 131 0.16 :(
## 2: 4.5 NA 0.3099433 -0.003373171 159 0.91 :(
## 3: 7.5 NA 0.3587827 0.013769725 159 0.7 :(
## 4: 10.5 NA 0.4023180 0.001613222 159 0.96 :(
## 5: 13.5 NA 0.4239140 0.056350607 159 0.16 :(
## 6: 16.5 NA 0.4203972 0.060432004 159 0.071 .
## 7: 19.5 NA 0.3821550 0.064658014 159 0.064 .
## 8: 22.5 NA 0.3478220 0.026227101 159 0.47 :(
## 9: 25.5 NA 0.3648829 0.084304485 159 0.018 *
## 10: 28.5 NA 0.3706005 0.078014462 159 0.045 *
## time error.diff shapes
## 1: 1.5 -0.074437040 16
## 2: 4.5 -0.003373171 16
## 3: 7.5 0.013769725 16
## 4: 10.5 0.001613222 16
## 5: 13.5 0.056350607 16
## 6: 16.5 0.060432004 16
## 7: 19.5 0.064658014 16
## 8: 22.5 0.026227101 16
## 9: 25.5 0.084304485 24
## 10: 28.5 0.078014462 24
